So I've been playing with the median problem today. Not sure why, but it
stuck in my head.
>import Data.List
>import Control.Monad.ST
>import Data.STRef
>import Control.Monad
I've been using the hashing algorithm that I described last night, but it's
quite slow. I must be missing something obvious. It really shouldn't be
slow at all!
So I have a median function:
>median
First I assume you know the range of your data.
> (min,max)
Then I ask you to figure out how many buckets you want to create, and which
of those buckets you actually want to fill. You should only fill the
buckets near the middle, based on an educated guess of the distribution of
your data, otherwise, you will end up wasting memory. I you guessed the
middle correctly, you will get back (Just median) otherwise, you will get
back Nothing.
> numBuckets guessedMiddle
Then you are to pass it your list.
> list =
> let
First I seperate out the values into a list of buckets, each one holding
values which are near to eachother. I can figure out the length of the list
at the same time, since I have to go through the whole thing anyways.
> (myBuckets,length) =
> buckets
> (min,max)
> numBuckets
> guessedMiddle
> list
The buckets are set up, so that the first bucket has the lowest values, and
the last bucket has the highest values. Each bucket, is a tuple, of it's
length and it's contents. We fold across the list of buckets, accumulating
the "index so far", until we find the bucket in which the median must
reside.
> Right (medianBucket,stubLen) =
> foldr
> (\thisBucket@(thisBucketLen,_) eitheriOrMedianBucket ->
> case eitheriOrMedianBucket of
> Left i ->
> if i + thisBucketLen > (length `div` 2)
> then Right (thisBucket, thisBucketLen-((length `div` 2) - i))
> else Left (i + thisBucketLen)
> _ -> eitheriOrMedianBucket)
> (Left 0)
> myBuckets
> in
We then sort the bucket in which the median must reside, and we then find
the median the normal way. This should be faster, since we only had to sort
one bucket, rather than the entire list. It is not, so there must be
something horrible going on.
> case snd medianBucket of
> Just medianBucket' ->
> Just (sort medianBucket' `genericIndex` stubLen)
> Nothing -> Nothing
Here is my actual function for seperating the values out into buckets.
>buckets
> (min,max)
> numBuckets
> (guessedMiddleStart,guessedMiddleEnd)
> list =
> runST $ do
> lengthRef <- newSTRef 0
First we create a list of empty buckets. Since it would be a waste of
memory to actually fill the buckets near the edges of our distribution(where
we are not likely to find our median), our buckets contain Maybe lists, and
the buckets which are outside of our guessed bucket range will be filled
with Nothing.
> buckets' <- mapM
> (\n->
> newSTRef
> (0,
> if n >= guessedMiddleStart && n <= guessedMiddleEnd
> then Just []
> else Nothing))
> [0..numBuckets]
Then we go through the buckets, figuring out which bucket to put a given
value into. We calculate the length at the same time.
> forM_ list $ \number -> do
> let
Figure out which bucket to put this into.
> bucket =
> whichBucket
> (min,max)
> numBuckets
> number
Increment length.
> modifySTRef
> lengthRef
> (+1)
Put the value into the appropriate bucket.
> modifySTRef
> (buckets' `genericIndex` bucket) --Obvious optimization, use an array
and not a list.
> (\(oldLen,oldListMaybe)->
> case oldListMaybe of
> Just oldList ->
> (oldLen+1,Just (number:oldList))
> Nothing -> (oldLen + 1, Nothing))
> filledBuckets <- mapM readSTRef buckets'
> length <- readSTRef lengthRef
> return (filledBuckets,length)
>whichBucket (min,max) numBuckets number =
> (number - min)
> `div`
> ((max - min) `div` numBuckets)
So I created a little test scenario.
>someListNumBuckets = 100
>someListGuessedMiddle = (45,55)
>someListLength = genericLength someList
And found that this:
>realMedian = sort someList `genericIndex` (someListLength `div` 2)
Is actually faster ^_^ :O
*Main> realMedian
500
(7.18 secs, 2031543472 bytes)
Than this:
>someListMedian =
> median
> (someListMin,someListMax)
> someListNumBuckets
> someListGuessedMiddle
> someList
*Main> someListMedian
Just 500
(37.77 secs, 15376209200 bytes)
>someListMin :: Integer
>someListMin = 0
>someListMax :: Integer
>someListMax = 1000
>someList :: [Integer]
>someList =
> concatMap
> (\n->intersperse n [someListMin..someListMax])
> [someListMax,someListMax-1..someListMin]
---------- Původní zpráva ----------
Od: timothyhobbs at seznam.cz
Datum: 3. 9. 2012
Předmět: Re: [Haskell-cafe] hstats median algorithm
"
It really depends on how you are reading in the data and what you plan to do
with it besides taking the median. Obviously, if you read in your data as
an ordered list things can be done O(n) without any trouble.
In another case, if you already know the range, you can make a hash table
and start at the middle bucket and move outwards. That will be O(n) + O
(bucketsize log(bucketsize)) given that the middle bucket is non empty and
I'm not horribly mistaken.
Tim
---------- Původní zpráva ----------
Od: David Feuer <david.feuer at gmail.com>
Datum: 3. 9. 2012
Předmět: Re: [Haskell-cafe] hstats median algorithm
"
I was thinking it should offer a randomized version (taking a generator),
since randomized median algorithms provide the best expected performance. It
could also offer a deterministic version using some variant of median-of-
medians, intended for long lists. I guess it probably should retain the
naive version for short lists. Some benchmarking would suggest a good
cutoff. Has anyone come up with a better practical deterministic O(n)
algorithm since median-of-medians? I saw a paper by Dorit Dor on reducing
the number of comparisons to a bit under 3n, which also showed a lower bound
of a bit over 2n, but the algorithm she gives strikes me as far too complex
to be practical.
On Sep 1, 2012 9:17 PM, "Gershom Bazerman" <gershomb at gmail.com
(mailto:gershomb at gmail.com)> wrote:
" In my experience, doing much better than the naive algorithm for median is
surprisingly hard, and involves a choice from a range of trade-offs. Did you
have a particular better algorithm in mind?
If you did, you could write it, and contact the package author with a patch.
You also may be able to find something of use in Edward Kmett's order-
statistics package: http://hackage.haskell.org/package/order-statistics
(http://hackage.haskell.org/package/order-statistics)
Cheers,
Gershom
On 9/1/12 3:26 PM, David Feuer wrote:
" The median function in the hstats package uses a naive O(n log n)
algorithm. Is there another package providing an O(n) option? If not,
what would it take to get the package upgraded?
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